Tech
Mistral bets on ‘build-your-own AI’ as it takes on OpenAI, Anthropic in the enterprise
Most enterprise AI projects fail not because companies lack the technology, but because the models they’re using don’t understand their business. The models are often trained on the internet, rather than decades of internal documents, workflows, and institutional knowledge.
That gap is where Mistral, the French AI startup, sees opportunity. On Tuesday, the company announced Mistral Forge, a platform that lets enterprises build custom models trained on their own data. Mistral announced the platform at Nvidia GTC, Nvidia’s annual technology conference, which this year is focused heavily on AI and agentic models for enterprise.
It’s a pointed move for Mistral, a company that has built its business on corporate clients while rivals OpenAI and Anthropic have soared ahead in terms of consumer adoption. CEO Arthur Mensch says Mistral’s laser focus on the enterprise is working: The company is on track to surpass $1 billion in annual recurring revenue this year.
A big part of doubling down on enterprise is giving companies more control over their data and their AI systems, Mistral says.
“What Forge does is it lets enterprises and governments customize AI models for their specific needs,” Elisa Salamanca, Mistral’s head of product, told TechCrunch.
Several companies in the enterprise AI space already claim to offer similar capabilities, but most focus on fine-tuning existing models or layering proprietary data on top through techniques like retrieval augmented generation (RAG). These approaches don’t fundamentally retrain models; instead, they adapt or query them at runtime using company data.
Mistral, by contrast, says it is enabling companies to train models from scratch. In theory, this could address some of the limitations of more common approaches — for example, better handling of non-English or highly domain-specific data, and greater control over model behavior. It could also allow companies to train agentic systems using reinforcement learning and reduce reliance on third-party model providers, avoiding risks like model changes or deprecation.
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Forge customers can build their custom models using Mistral’s wide library of open-weight AI models, which includes small models such as the recently introduced Mistral Small 4. According to Mistral co-founder and chief technologist, Timothée Lacroix, Forge can help unlock more value out of its existing models.
“The trade-offs that we make when we build smaller models is that they just cannot be as good on every topic as their larger counterparts, and so the ability to customize them lets us pick what we emphasize and what we drop,” Lacroix said.
Mistral advises on which models and infrastructure to use, but both decisions stay with the customer, Lacroix said. And for teams that need more than guidance, Forge comes with Mistral’s team of forward-deployed engineers who embed directly with customers to surface the right data and adapt to their needs — a model borrowed from the likes of IBM and Palantir.
“As a product, Forge already comes with all the tooling and infrastructure so you can generate synthetic data pipelines,” Salamanca said. “But understanding how to build the right evals and making sure that you have the right amount of data is something that enterprises usually don’t have the right expertise for, and that’s what the FDEs bring to the table.”
Mistral has already made Forge available to partners, including Ericsson, the European Space Agency, Italian consulting company Reply, and Singapore’s DSO and HTX. Early adopters also include ASML, the Dutch chipmaker that led Mistral’s Series C round last September at a €11.7 billion valuation (approximately $13.8 billion at the time).
These partnerships are emblematic of what Mistral expects Forge’s main use cases to be. According to Mistral’s chief revenue officer Marjorie Janiewicz, these include governments who need to tailor models for their language and culture; financial players with high compliance requirements; manufacturers with customization needs; and tech companies that need to tune models to their code base.
Tech
Amazon working on new smartphone with Alexa at its core, report says
Looks like Amazon’s getting back into the smartphone game. More than 11 years after the e-commerce giant pulled the plug on its failed first effort, the Fire Phone, the company is now developing a new smartphone codenamed “Transformer,” Reuters reported, citing anonymous sources.
The device is being developed by the company’s Devices and Services division, and it would feature personalized features that would make it easier to use Amazon’s suite of apps, including Amazon Shopping, Prime Video, and Prime Music, the report said.
The smartphone would also support Alexa, the smart home assistant that Amazon has been investing heavily in, adding AI chops and expanding support to work with most of the company’s devices. AI features are said to be a big focus for the smartphone, which is being seen internally as a way to encourage Amazon customers to use its AI products, Reuters reported.
The smartphone is said to be developed by a relatively new unit within the Devices division called ZeroOne, which is led by J Allard, a former Microsoft executive who helped create the Xbox.
The news comes as Amazon has been going all-in on AI, investing $50 billion into OpenAI recently, and projecting $200 billion in capital expenditures toward its AI, chips, and robotics efforts in 2026.
The company spent more than a year revamping its Alexa assistant with generative AI features, finally launching it this February as Alexa+. The assistant keeps its smart home chops, and can now do most things that other AI chatbots can — like planning an itinerary for a trip, updating a shared calendar, finding and saving recipes to a library, making movie recommendations, helping with homework, exploring a topic, and more.
Amazon declined to comment.
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Tech
Cyberattack on vehicle breathalyzer company leaves drivers stranded across the US
A cyberattack on a U.S. vehicle breathalyzer company has left drivers across the United States stranded and unable to start their vehicles.
The company, Intoxalock, says on its website that it is “currently experiencing downtime” after a cyberattack on March 14. Intoxalock sells breathalyzer devices that fit into vehicle ignition switches, and is used by people who are required to provide a negative alcohol breath sample to start their car.
Intoxalock spokesperson Rachael Larson confirmed to TechCrunch that the company had been hit by a cyberattack. Larson said the company took steps to “temporarily pause some of our systems as a precautionary measure.”
These breathalyzer devices need to be calibrated every few months or so, but the cyberattack has left Intoxalock unable to perform these calibrations. The company said customers whose devices require calibration may experience delays starting their vehicles.
Drivers posting on Reddit say that cars are unable to start if they miss a calibration, effectively locking drivers out of their vehicles.
According to local news reports across Maine, drivers are experiencing lockouts and some have been unable to start their vehicles. One auto shop in Middleboro told WCVB 5 in Boston that it has had cars parked in its lot all week due to the cyberattack.
News reports from across the United States show drivers are affected from New York to Minnesota, and drivers have been unable to drive because their vehicle-based breathalyzers cannot be immediately calibrated.
Intoxalock would not say what kind of cyberattack it was experiencing, such as ransomware or if there was a data breach, or whether it had received any communications from the hackers, including any ransom demands. The company’s technology is used in 46 states, its website says, and it claims to provide services to 150,000 drivers every year.
Intoxalock did not provide an estimated timeline for its recovery.
Tech
AI startups are eating the venture industry and the returns, so far, are good
Well, the data is out. AI startups accounted for 41% of the $128 billion in venture dollars raised by companies on Carta last year — a record-high annual share. In a sense, though, we knew that. Investors last year were voracious in deploying capital to AI startups, to the point that 10% of startups accounted for half of the funding.
Those startups included Anthropic, OpenAI, and xAI, which raised double-digit billions last year at sky-high valuations. Actually, they are still raising at an even more astounding velocity. In January, xAI raised a $20 billion Series E. In February, OpenAI snagged a $110 billon round, one of the largest private rounds ever raised, bringing the company closer than ever to a $1 trillion valuation.
Size-wise, in between OpenAI and xAI was Anthropic, which raised a $30 billion Series G last month at a $380 billion valuation. OpenAI and Anthropic accounted for a heavy chunk of the $189 billion in global venture capital raised last month and, alongside xAI, have teased IPOs for later this year that have left investors foaming at the mouth.
The state of the venture market is now K-shaped — or bifurcated — in which capital remains concentrated in a select few firms that then back a handful of companies, while everyone else is, well, kinda just there.
“While funding rounds have gotten slightly harder to raise, the capital for each round has increased,” Peter Walker, head of insights at Carta, told TechCrunch. “So fewer bets, but more capital. AI startups are raising bigger rounds not because they have lots of employees — they don’t — but because the cost of running AI models is high.”
The latest Carta data also shows that funds raised in 2023 and 2024 (after the launch of ChatGPT in late 2022) have posted the highest internal rate of return (IRR), compared with the declining IRR of funds raised between 2017 and 2020. The report views the increased IRR over the past few years as a positive indicator for the funds backing some of the leading startups emerging from this AI moment.
“It’s promising that the younger funds have seen IRR start strong,” Walker said, adding, however, that there were a few factors to consider. For one, he said, newer funds might look like they are doing well on paper because if they invested in a seed round, for example, and that company went on to raise a Series A at a higher valuation, then on paper it looks like the investor made high returns in a short time period.
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“This pushes IRR up,” Walker said. “It is also likely that the portfolios of the more recent vintage funds are full of AI-native startups in a way that the portfolios of 2021/2020 funds are not.”
Time will tell if this early enthusiasm will translate into real returns for investors via exits like blockbuster IPOs or big-dollar acquisitions, or if we are merely in the hype phase of a bubble that will eventually pop.
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